Most patient monitoring is typically implemented by measuring and observing a plurality of physiological parameters such as: ECG (Electrocardiogram), Pulse Oximetry (involving measuring blood oxygen levels or SpO2), Respiration (derived from ECG signal or from other parameters), Invasive Blood Pressure (or IBP that involves direct measurement of blood pressure from an indwelling catheter), and Non-Invasive Blood Pressure (or NIBP that involves use of automated oscillometric methods).
Typically these physiological parameters have a set of vital signs and derived measurements which can be configured to alert a caregiver if the measured values move outside of configured ranges. Each parameter has a plurality of alarms that can be considered to be of different priorities. However, prior art methods and systems tend to treat each of these parameters independently for deciding/determining alarm situations or fail to provide a workable mechanism for effectively determining whether an alarm state, derived from the signal of a particular patient monitoring device, is false, likely to be false, or sufficiently indicative of the state of a patient to warrant alerting a caregiver. As a result, the clinical user may experience an unacceptable number of alarms within these patient monitoring systems. The caregiver will ultimately see a conglomeration of alarm states from various fluctuations for each of the parameters, leading to unnecessary distraction and caregiver apathy regarding alarms.
Accordingly there is need in the art for methods and systems that effectively suppress or demote the number of false alarms the user sees and to make sure that when the system alarms there is a significant probability that the patient requires immediate attention.